MakerPlace by Michaels competes directly with marketplaces such as Etsy and follows the retailer’s addition in March of a third-party marketplace for

Arts and crafts retailer Michaels opens its version of Etsy called MakerPlace

But while local retailers like Jeckel are ramping up hiring efforts, and national giants like Target and Macy’s plan to hire 100,000 and 38,000 seasonal workers, respectively, the seasonal surge of retail employees is likely to be smaller overall this year.

Holiday season hiring expected to slacken on Long Island

Macy's (M) ended the recent trading session at $12.18, demonstrating a +1.33% swing from the preceding day's closing price. The stock exceeded the S&P 500, which registered a gain of 0.65% for the day.

Macy's (M) Outpaces Stock Market Gains: What You Should Know

Beauty company Lush has launched a partnership with Ulta Beauty to sell its products on the retailer’s website.

Lush launches wholesale partnership with Ulta Beauty

RFID gives retailers crime fighting capabilities they have wanted for decades, says the Managing Director of the Loss Prevention Research Council

Famous Criminologist Says Retailers Vastly Underuse RFID Technology

A man was arrested on Thursday, Oct. 26, for allegedly pointing a gun at a Valley Plaza Mall security guard after he was seen trying to steal merchandise at

Man arrested for allegedly pointing gun at Valley Plaza Mall security after attempted theft at Macy's

YouTube’s privacy policy is available here and YouTube’s terms of service is available here.

Shopping, movie premieres and Santa: Briarwood Mall celebrates 50 years in Ann Arbor

This holiday season, Macy’s highlights its dedication to youth empowerment through its partnership with Big Brothers Big Sisters (BBBS). As part of the brand’s social purpose platform, Mission Every One,

Macy’s Celebrates the Holiday Season With Big Brothers Big Sisters

Macy’s Inc. chief legal officer Elisa Garcia is getting ready to cap a nearly 40-year career and prepare for a busy travel itinerary.

Macy’s Legal Chief Retires as Veteran Retailer Tees Up New CEO

Macy's, Inc., an omnichannel retail organization, operates stores, websites, and mobile applications under the Macy's, Bloomingdale's, and bluemercury brands. It sells a range of merchandise, including apparel and accessories for men, women, and kids; cosmetics; home furnishings; and other consumer goods. As of February 1, 2020, the company operated 775 store locations in 43 states, the District of Columbia, Puerto Rico, and Guam. It also operates in Dubai, the United Arab Emirates and Al Zahra, Kuwait under the license agreements. The company was formerly known as Federated Department Stores, Inc. and changed its name to Macy's, Inc. in 2007. Macy's, Inc. was founded in 1830 and is based in New York, New York.

Macy's

Authors:
(1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de;
(2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de;
(3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de;
(4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Literature Review Objectives and Research Questions Methods Results Discussion Conclusion, Research Data, Acknowledgments, and References METHODS As illustrated in Figure 1, this study was conducted in five steps. In the first step, the query sets were generated from previously collected Google Trends data. The daily trends for both Germany and the US were collected daily at 3 am CET from November 10th, 2021, until March 31st, 2022. Since the daily number of trending queries can vary, the query sets for Germany and the US are not the same size. Two thousand nine hundred sixteen trending queries for Germany and 2,819 for the US were initially collected. However, since some topics of interest are longer lived than a single day, the same topics can be trending on multiple days. Therefore, after removing duplicates, our query sets consist of 1,821 queries for Germany and 2,126 queries for the US. The queries have a wide range, including topics like celebrities (e.g., "Sandra Bullock"), sports (e.g., "NFL," "Liverpool vs. Southampton"), seasonal events (e.g., "Macy's Thanksgiving Day Parade," "Restaurants open on Thanksgiving"), headline news (e.g., "TikTok classaction lawsuit settlement"), and more. In step 2, for each query, the top 10 results from all four search engines were collected. We made sure to request results for the appropriate language and location by using URL parameters. Furthermore, we decided to limit the comparison to the top 10 results because users usually only consider these. Again, for our study, only the implications for the user perception are relevant. The Web scraping component of the Relevance Assessment Tool (RAT; Sünkler et al., 2022) was used to collect the data. The scraping took place between April 6th and 9th, 2022, with 230,315 results (Germany: 109,604; US: 120,711) gathered. It is important to note that only organic search results were considered. A fair comparison can be ensured when ranked result lists instead of vertical inserts of universal search results (e.g., Google News and Bing News) are considered. We also ignored ads since they are not part of the search engine's web index. During step 3, the python library urllib was used to get the domain of each search result. To ensure a fair comparison between all search engines, in step 4, we removed results for any query that had less than ten results in any of the search engines. Additionally, the data was checked and stripped of any errors that might have occurred during the collection, like duplicate results. This resulted in a further reduced dataset consisting of 1,672 queries and 66,880 results for Germany and 1,865 queries and 74,600 results for the US. Lastly, the extraction of top-level domains was further refined in this step. We used basic string matching to unify URLs that pointed to the same domains. For example, the following three URLs would have been counted as separate domains without the string matching: https://www.facebook.com, "http://www.facebook.com", and "http://www.facebook.com/". Finally, in step five, we used different methods of comparing the data collected. An initial comparison is made by using simple descriptive statistics. To measure source concentration, we adapt the Gini coefficient, a measure of statistical dispersion used to measure income or wealth inequality (Gini, 1936). The Gini coefficient is a single number ranging from 0 to 1, where 0 represents perfect equality, and 1 is the maximum inequality. As Ortega et al. (2008) demonstrated, adapting this measure to the distribution of values in different domains is appropriate. In the case of search result sets, this allows us to compare the various search engines easily. The lower the Gini coefficient, the more equally the results are distributed over all domains contributing to a particular result set. Following that, we calculate the Jaccard similarity index (González et al., 2008). Puschmann (2019) demonstrates the usage of the Jaccard index to measure the similarity between two result sets. The Jaccard index is a single number ranging from 0 to 1, where 0 means that the two sets are entirely dissimilar, while 1 means that they are identical. We calculate the Jaccard index for every two-way combination of our four search engines (e.g., Google and Bing, Bing and Metager, etc.) for all stages between the top 1 and top 10 results (e.g., Google and Bing position 1 to 5). This paper is available on arxiv under CC 4.0 license. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Authors: (1) Yagci, Nurce, HAW Hamburg, Germany & nurce.yagci@haw-hamburg.de; (2) Sünkler, Sebastian, HAW Hamburg, Germany & sebastian.suenkler@haw-hamburg.de; (3) Häußler, Helena, HAW Hamburg, Germany & helena.haeuessler@haw-hamburg.de; (4) Lewandowski, Dirk, HAW Hamburg, Germany & dirk.lewandowski@haw-hamburg.de. Table of Links Abstract and Introduction Abstract and Introduction Literature Review Literature Review Objectives and Research Questions Objectives and Research Questions Methods Methods Results Results Discussion Discussion Conclusion, Research Data, Acknowledgments, and References Conclusion, Research Data, Acknowledgments, and References METHODS As illustrated in Figure 1, this study was conducted in five steps. In the first step, the query sets were generated from previously collected Google Trends data. The daily trends for both Germany and the US were collected daily at 3 am CET from November 10th, 2021, until March 31st, 2022. Since the daily number of trending queries can vary, the query sets for Germany and the US are not the same size. Two thousand nine hundred sixteen trending queries for Germany and 2,819 for the US were initially collected. However, since some topics of interest are longer lived than a single day, the same topics can be trending on multiple days. Therefore, after removing duplicates, our query sets consist of 1,821 queries for Germany and 2,126 queries for the US. The queries have a wide range, including topics like celebrities (e.g., "Sandra Bullock"), sports (e.g., "NFL," "Liverpool vs. Southampton"), seasonal events (e.g., "Macy's Thanksgiving Day Parade," "Restaurants open on Thanksgiving"), headline news (e.g., "TikTok classaction lawsuit settlement"), and more. In step 2, for each query, the top 10 results from all four search engines were collected. We made sure to request results for the appropriate language and location by using URL parameters. Furthermore, we decided to limit the comparison to the top 10 results because users usually only consider these. Again, for our study, only the implications for the user perception are relevant. The Web scraping component of the Relevance Assessment Tool (RAT; Sünkler et al., 2022) was used to collect the data. The scraping took place between April 6th and 9th, 2022, with 230,315 results (Germany: 109,604; US: 120,711) gathered. It is important to note that only organic search results were considered. A fair comparison can be ensured when ranked result lists instead of vertical inserts of universal search results (e.g., Google News and Bing News) are considered. We also ignored ads since they are not part of the search engine's web index. During step 3, the python library urllib was used to get the domain of each search result. To ensure a fair comparison between all search engines, in step 4, we removed results for any query that had less than ten results in any of the search engines. Additionally, the data was checked and stripped of any errors that might have occurred during the collection, like duplicate results. This resulted in a further reduced dataset consisting of 1,672 queries and 66,880 results for Germany and 1,865 queries and 74,600 results for the US. Lastly, the extraction of top-level domains was further refined in this step. We used basic string matching to unify URLs that pointed to the same domains. For example, the following three URLs would have been counted as separate domains without the string matching: https://www.facebook.com, "http://www.facebook.com", and "http://www.facebook.com/". Finally, in step five, we used different methods of comparing the data collected. An initial comparison is made by using simple descriptive statistics. To measure source concentration, we adapt the Gini coefficient, a measure of statistical dispersion used to measure income or wealth inequality (Gini, 1936). The Gini coefficient is a single number ranging from 0 to 1, where 0 represents perfect equality, and 1 is the maximum inequality. As Ortega et al. (2008) demonstrated, adapting this measure to the distribution of values in different domains is appropriate. In the case of search result sets, this allows us to compare the various search engines easily. The lower the Gini coefficient, the more equally the results are distributed over all domains contributing to a particular result set. Following that, we calculate the Jaccard similarity index (González et al., 2008). Puschmann (2019) demonstrates the usage of the Jaccard index to measure the similarity between two result sets. The Jaccard index is a single number ranging from 0 to 1, where 0 means that the two sets are entirely dissimilar, while 1 means that they are identical. We calculate the Jaccard index for every two-way combination of our four search engines (e.g., Google and Bing, Bing and Metager, etc.) for all stages between the top 1 and top 10 results (e.g., Google and Bing position 1 to 5). This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Comparative Analysis of Search Engine Results Using Google Trends Data

This article was , an independent, nonprofit organization that works side by side with consumers for truth, transparency and fairness in the marketplace. . copublished with Consumer Reports Learn more here By now most internet users know their online activity is constantly tracked. No one should be shocked to see ads for items they previously searched for, or to be asked if their data can be shared with an unknown number of “partners.” But what is the scale of this surveillance? Judging from data collected by Facebook and by non-profit consumer watchdog Consumer Reports, it’s massive, and examining the data may leave you with more questions than answers. newly described in a unique study Using a panel of 709 volunteers who shared archives of their Facebook data, Consumer Reports found that a total of 186,892 companies sent data about them to the social network. On average, each participant in the study had their data sent to Facebook by 2,230 companies. That number varied significantly, with some panelists’ data listing over 7,000 companies providing their data. The Markup helped Consumer Reports recruit participants for the study. Participants downloaded an archive of the last three years of their data from their Facebook settings, then provided it to Consumer Reports. By collecting data this way, the study was able to examine a form of tracking that is normally hidden: so-called “server-to-server” tracking, in which personal data goes from a company’s servers to Meta’s servers. Another form of tracking, in which Meta tracking pixels are placed on company websites, is visible to users’ browsers. Because the data came from a self-selected group of users, and because the results were not demographically adjusted, the study does not “make any claims about how representative this sample is of the U.S. population as a whole,” Consumer Reports noted. Participants were also likely more privacy conscious and technically inclined than typical users and more likely to be members of Consumers Reports. Despite its limitations, the study offers a rare look, using data directly from Meta, on how personal information is collected and aggregated online. Meta spokesperson Emil Vazquez defended the company’s practices. “We offer a number of transparency tools to help people understand the information that businesses choose to share with us, and manage how it’s used,” wrote Vazquez in an emailed statement to The Markup. While Meta does provide transparency tools like the one that enabled the study, Consumer Reports identified problems with them, including that the identity of many data providers is unclear from the names disclosed to users and that companies that provide services to advertisers are often allowed to ignore opt-out requests. One company appeared in 96% of participants’ data: the San Francisco-based data broker LiveRamp. But the companies sharing your online activity to Facebook aren’t just little-known data brokers. Retailers like Home Depot, Walmart and Macy’s all were in the top 100 most frequently-seen companies in the study. Credit reporting and consumer data companies such as Experian and TransUnion’s Neustar also made the list, as did Amazon, Etsy and PayPal. LiveRamp did not respond to a request for comment. What Exactly Does This Data Contain? The data examined by Consumer Reports in this study comes from two types of collection: events and custom audiences. Both categories include information about what people do outside of Meta’s platforms. Custom audiences allow advertisers to upload customer lists to Meta, often including identifiers like email addresses and mobile advertising IDs. These customers, and so-called “lookalike audiences” made up of similar people, can then be targeted with ads on Meta’s platforms. The other category of data collection, “events,” describes interactions that the user had with a brand, which can occur outside of Meta’s apps and in the real world. Events can include visiting a page on a company’s website, leveling up in a game, visiting a physical store, or purchasing a product. These signals originate from Meta software code included in many mobile apps, their tracking pixel, which is included on many websites, and from server-to-server tracking, where a company’s server passes data to a Meta server. The Markup has written extensively about the and how it has been used to surveil people as they , , , , and . Meta Pixel dial suicide hotlines buy their groceries take the SATs file their taxes book appointments with their doctors Website owners can configure the pixel to track user website interactions such as searches or filling out a form, sending each action to Meta, even if the user doesn’t have an account on Facebook. Although research tools like The Markup’s “Pixel Hunt” can detect the Meta pixel or SDK tracking, there is no way for a consumer to monitor the traffic between a company’s server and Meta’s. This Consumer Reports study looks at server-to-server data along with the rest. How Can I See My Data? Facebook users can browse through the list of companies that have sent their data to Facebook by going to: https://accountscenter.facebook.com/info_and_permissions From this menu, users can either download or access their information. To see the companies outside of Meta that have been sharing your information, select “Your activity off Meta technologies,” then “Recent activity.” You might be prompted to re-enter your password at this point. This view will show you the number of recent connections between various third-party businesses you visited and Meta, and examples of the activity that was shared. You can choose to prevent future sharing by the company by selecting “Disconnect.” To see detailed information about these interactions, . request a copy of your data But after jumping through the many hoops required to actually get your hands on this data, you may still be left with lingering questions. For example, in addition to easily recognizable company names, more than 7,000 companies in Consumer Reports’ study were named using unreadable gibberish, or just numbers that don’t mean anything to most users. Even those companies with readable names often did not include links to the company website. Some company names were ambiguous, such as “Viking,” which could be a number of different businesses. “This type of tracking which occurs entirely outside of the user’s view is just so far outside of what people expect when they use the internet,” Caitriona Fitzgerald, Deputy Director of the Electronic Privacy Information Center, told The Markup in an interview. Fitzgerald said that while users are likely aware that Meta knows what they are doing while they are on Facebook and Instagram, “they don’t expect Meta to know what stores they walk into or what news articles they’re reading or every site they visit online.” In the report, Consumer Reports calls for a number of policy proposals covering data collection practices, some of which could be part of a national digital privacy law, something that the organization has long advocated for. Among the recommendations specifically aimed at Meta’s technology and the advertisers who use it: Require companies to adopt “data minimization” strategies which call for the collection of the absolute minimum amount of data needed to provide the service being offered. Expand the powers of “authorized agents” to act on behalf of consumers to act on their rights. is a mobile app by Consumer Reports that allows users to quickly opt-out of and delete data from a long list of companies and data brokers on behalf of users; the app takes advantage of state privacy laws’ authorized agent provisions. Permission Slip Increase ad transparency by creating ad archives that allow the public to see all ads that have been served to users on a platform, following the lead of the EU's . Digital Services Act Existing Meta transparency tools should improve the quality and readability of the data that is made available so consumers can actually act on the information they review. Fitzgerald echoed these recommendations, saying the problem lies with the fact that the burden is on the consumer to take action to prevent this data collection. Even a “global opt-out” mechanism allowing users to avoid having their data shared is not enough because “that still requires the user to take an action to protect their privacy. A lot of people are not going to have the time or knowledge to do that,” said Fitzgerald. Vazquez, Meta’s spokesperson, said the company would “continue to invest in data minimization technologies to keep up with evolving expectations. As we cover in our terms, businesses are responsible for getting permission to share people’s information with companies like ours.” For now, the lack of a federal privacy law leaves consumers in most states with few options. “I think people should be encouraging their elected officials to pass privacy laws that require businesses to change some of these business practices to stop this ubiquitous tracking of our every click and every movement,” said Fitzgerald. Credits: , , , , , Jon Keegan Carlo Cadenas Maria Puertas Gabriel Hongsdusit Ryan Tate Michael Reilly Also published here Photo by on Amanda Dalbjörn Unsplash

If You're a Facebook User, Thousands of Companies Are Watching You

আপনি যদি একজন ফেসবুক ব্যবহারকারী হন, হাজার হাজার কোম্পানি আপনাকে দেখছে

Wenn Sie ein Facebook-Nutzer sind, werden Sie von Tausenden Unternehmen beobachtet

Si eres usuario de Facebook, miles de empresas te están observando

Si vous êtes un utilisateur de Facebook, des milliers d'entreprises vous surveillent

यदि आप फेसबुक उपयोगकर्ता हैं, तो हजारों कंपनियां आप पर नजर रख रही हैं

あなたが Facebook ユーザーなら、何千もの企業があなたに注目しています

당신이 Facebook 사용자라면 수천 개의 회사가 당신을 지켜보고 있습니다.

Se você é um usuário do Facebook, milhares de empresas estão observando você

Если вы пользователь Facebook, за вами наблюдают тысячи компаний

Facebook Kullanıcısıysanız Binlerce Şirket Sizi İzliyor

Nếu bạn là người dùng Facebook, hàng nghìn công ty đang theo dõi bạn

如果您是 Facebook 用户，成千上万的公司正在关注您

248 Stories To Learn About Cyber Threats

"There is nothing new that is not old." Technological progress does not stand still, and neither do fraud methods. Not long ago, a by Akamai about a new technique for skimming. , where it all started, and what new fraudsters have invented will be discussed in our article. report was published Skimming Magecart: What Kind of Skimming Is That? Skimming is a fraudulent technique where criminals steal information from bank cards. In the early stages, criminals would install specialized card readers and overlay keypads on bank terminals to later clone the cards. Skimmers come in two types: some gather information from various users, while others immediately transmit card data to fraudsters through a radio channel or via an embedded SIM card over cellular networks. However, with the advancement of technology, this type of fraud has shifted to the Internet. Criminals inject malicious code into websites, typically on payment processing pages or other areas where users enter sensitive information, such as bank card details. Subsequently, when visitors enter their details on infected pages, criminals intercept this information. This technique is known as Magecart. Attackers seek out susceptible payment processing websites and insert malicious code into payment processing pages or other pages where customers enter bank card information. When information is entered, the malicious code intercepts it and transfers it to a remote server. Magecart has carried out several high-profile attacks on well-known e-commerce sites and third-party payment providers. The stolen data is usually sold on the underground market or used for fraudulent transactions. Regular security audits, website software updates, suspicious activity monitoring, and using security precautions security measures to identify and thwart such assaults are all part of the defense against Magecart attacks. There are known incidents in history involving companies such as Macy's, Puma, and British Airways. What's up with the 404? The skimmer penetrates the program by introducing a script featuring a code snippet that closely mirrors Meta Pixel, a renowned service for monitoring visitor activity, and incorporates several supplementary lines of code. This technique enables skimmers to avoid detection by conducting static analysis by external scanners and researchers. The extra lines of seemingly innocuous code make it easier to submit a PNG image with a Base64 encoded message attached at the end. This string is later retrieved, decoded, and converted into a JavaScript code snippet, which is executed by the loader code snippet. Facebook Based on this approach, a new method has been developed that involves opening the icon page via an absolute path, which provides a direct link to a "Not Found" page. This page then accesses a compromised section of code, which is then commented out with a special key, hiding the malicious code inside. This can happen when a site uses a third-party payment service that implements a payment form in an external iframe or on an external page. To bypass such scenarios, an attacker creates a fake form very similar to the original payment form and overlays it. When the user enters data into the fake form, an error appears, the fake form is hidden, the original payment form is opened, and the user is prompted to re-enter payment data. In this way, the old method of fraud and the new one are combined. How do you protect yourself? Buyers should pay attention to the address bar of the resource where the payment is made and uncharacteristic errors in the input form. Since thieves can utilize an active card with an empty balance for other fraudulent schemes, like money laundering, you immediately reissue the card and move all available funds to a savings or savings account as soon as you think you have been the victim of fraud. The cybercrime unit should pay attention to the new scheme and test systems for penetration.

Safeguard Your 404 Page Against Magecart’s New Technique

Dalyrimple Goes Wrong

In today's digital era, the abundance of data has transformed the way businesses approach marketing. Traditional methods of broad-brush advertising and guesswork are being replaced by a more precise and effective approach known as data-driven marketing. By harnessing the power of big data, companies can now create highly targeted campaigns that deliver personalized experiences to their customers. In this blog post, we will delve into the realm of data-driven marketing and explore how it can revolutionize your marketing strategies. The concept of data-driven marketing revolves around leveraging vast amounts of data collected from various sources to gain valuable insights into customer behavior, preferences, and needs. With the rise of online platforms, social media, e-commerce, and IoT devices, the amount of data generated is staggering. However, it is not the volume of data that matters; it is the ability to analyze and interpret it effectively that unlocks its potential. Big data, characterized by its volume, velocity, and variety, provides marketers with a treasure trove of information. By tapping into this wealth of data, businesses can better understand their customers, make informed decisions, and develop targeted campaigns that resonate with their audience on a deeper level. The benefits of data-driven marketing are far-reaching, from increased customer engagement and loyalty to improved conversion rates and higher return on investment. Understanding Big Data in Marketing Data has become a powerful resource in today's digital era, capable of revolutionizing marketing techniques. Big data, in particular, has enormous promise for firms wanting to improve their campaigns and increase client engagement. To fully harness the potential of big data in marketing, it is critical to grasp its underlying ideas and how to use it successfully. What Is Big Data Big data refers to the large volume of information created from numerous sources, both organized and unstructured, that surpasses the capabilities of typical data processing techniques. \It is characterized by the three V's: volume, velocity, and variety. signifies the enormous amount of data generated daily, Volume refers to the speed at which data is produced and needs to be analyzed in real-time. Velocity indicates the diverse types of data, including text, images, videos, social media posts, and more. Variety Sources of Big Data in Marketing In the marketing realm, big data can be derived from numerous sources, providing valuable insights into consumer behavior, preferences, and trends. Some common sources include: Every customer touchpoint, such as website visits, social media engagements, email interactions, and customer service interactions, generates data that can be collected and analyzed. Customer Interactions: : Sales records, purchase histories, and customer loyalty program data provide valuable information about buying patterns, preferences, and customer lifetime value. Transactional Data : Data from external sources like social media platforms, online reviews, market research reports, and demographic data can offer a broader perspective on consumer trends and preferences. External Data Challenges and Considerations in Handling Big Data While big data presents immense opportunities, it also comes with its fair share of challenges. Some key considerations include: : Ensuring data accuracy and integrity is essential. Inaccurate or incomplete data can lead to flawed insights and misguided marketing decisions. Data Quality : Consolidating data from various sources and systems can be complex. Integrating and organizing data effectively is crucial to derive meaningful insights. Data Integration and Privacy: With the increasing focus on data privacy, organizations must take appropriate measures to protect customer information and comply with data protection regulations. Data Security : Analyzing big data requires specialized skills and robust technological infrastructure. Organizations need the right talent and resources to handle data effectively. Skills and Resources Leveraging Data for Targeted Campaigns Marketers may acquire significant insights into consumer behavior and preferences by using the power of big data, allowing them to design targeted and tailored marketing. Let's look at how data may be efficiently used for targeted marketing. Collecting and analyzing customer data To get started with data-driven marketing, you must first gather and evaluate relevant client data. This involves collecting data on consumer demographics, buying habits, internet activities, and interactions with your business. You may get a full picture of your audience by combining data from numerous sources, including CRM systems, social media platforms, website analytics, and customer surveys. : You may find common features among your target audience groups by evaluating demographic data such as age, gender, geography, and income level. Understanding client behavior, such as purchase history, browsing habits, and interaction patterns, enables you to personalize campaigns to meet the requirements of particular customers. Customer demographics and behavior : Tracking consumer interactions across many touchpoints, including website visits, email clicks, and social media engagements, gives significant data for campaign improvement. This information illustrates how clients connect with your brand, which channels are most successful, and what factors influence conversions. Tracking consumer interactions : In addition to online data, offline data sources like point-of-sale systems, customer service records, and loyalty programs may provide a more complete perspective of your consumers. This integration enables a holistic approach to data-driven marketing. Using online and offline data sources Segmentation and targeting Segmentation is a critical step in data-driven marketing that involves dividing your customer base into distinct groups based on shared characteristics and behaviors. With segmented data, you can tailor your campaigns to specific customer segments, ensuring your messages resonate with their needs and preferences. : Utilize data analysis tools and techniques to identify commonalities among your customers. This could involve clustering techniques, machine learning algorithms, or rule-based segmentation. By understanding the unique traits of each segment, you can develop targeted strategies. Defining customer segments based on data insights : Craft personalized messages and offers that align with the interests and pain points of each segment. By addressing their specific needs, you increase the chances of engagement and conversions. Personalization is key to building strong customer relationships and driving loyalty. Tailoring campaigns to specific customer segments : Leverage data to deliver personalized experiences through dynamic content, product recommendations, and tailored communication. Utilize marketing automation platforms that enable dynamic content insertion and audience targeting based on real-time data triggers. Personalization and customization techniques Predictive analytics and modeling Predictive analytics empowers marketers to forecast future customer behavior and optimize campaigns accordingly. By leveraging historical data and statistical modeling techniques, you can make data-driven decisions to enhance campaign effectiveness. Here are some tips for making predictive analytics : Analyze past data to identify patterns, trends, and correlations that can inform predictions about customer actions. This could involve predictive modeling techniques like regression, decision trees, or machine learning algorithms to forecast customer response to different marketing stimuli. Using data to predict customer behavior : Build models that can predict the likelihood of conversion, customer churn, or response to specific marketing efforts. These models can guide decision-making on campaign budgets, channel allocation, messaging, and timing to optimize results. Developing predictive models for campaign optimization : Test different variations of your campaigns using A/B testing methodologies. This allows you to compare the performance of different elements, such as headlines, visuals, calls to action, or pricing strategies. Analyze the results and iterate on your campaigns based on data-driven insights. A/B testing and iterative improvements Automation and optimization Automation plays a crucial role in maximizing the efficiency and impact of data-driven marketing. By leveraging marketing automation tools and technologies, you can streamline processes, deliver timely and relevant content, and optimize campaigns in real-time. : Adopt marketing automation platforms that enable the automation of repetitive tasks, such as email campaigns, social media posting, and lead nurturing. These tools facilitate data integration, audience segmentation, and personalized content delivery. Implementing marketing automation tools : Continuously monitor campaign performance and collect real-time data to gain insights into customer behavior and campaign effectiveness. Leverage this data to make data-driven optimizations, such as adjusting targeting parameters, refining messaging, or reallocating resources. Optimizing campaigns based on real-time data Marketing is an iterative process. Regularly monitor key performance indicators (KPIs) and campaign metrics to identify areas for improvement. Embrace a data-driven mindset, and be agile in making adjustments and optimizations based on insights gained from data analysis. Continuous monitoring and adjustment: Ethical Considerations in Data-driven Marketing Data-driven marketing has revolutionized the way businesses connect with their target audience and create personalized campaigns. However, as the use of big data becomes more prevalent, it is crucial to address the ethical considerations associated with its utilization. Here are some of the key ethical considerations in data-driven marketing and ways to ensure responsible and transparent practices. Privacy and Data Protection: : With the increasing concern for data privacy, regulations like the General Data Protection Regulation (GDPR) in the European Union and the California Consumer Privacy Act (CCPA) in the United States have been introduced. Adhering to these regulations is essential to protect customer data and avoid legal consequences. Compliance with regulations (e.g., GDPR, CCPA) : It's important to get people's consent before collecting and utilizing their data. It's critical to provide people opportunities to manage their data preferences and to give them clear information about how their data will be used. Informed permission and data transparency : A major concern should be protecting consumer data from unwanted access or breaches. Strong security measures should be put in place, and data should be anonymized wherever it is practical. Ensuring data security and anonymity Balancing Personalization and Customer Privacy: : While personalization enhances the customer experience, it is important to respect their boundaries. Avoid excessive targeting and intrusive practices that may make customers feel uncomfortable or violated. Respecting customer preferences and boundaries : Transparency is key to maintaining customer trust. Clearly communicate how data is collected, used, and shared. Being open about data practices helps build a trustworthy relationship with customers. Building trust through transparent practices : Empowering individuals with control over their data is essential. Offering easy-to-use opt-out mechanisms and allowing customers to modify their preferences or delete their data demonstrates respect for their choices and privacy. Providing opt-out options and control over data It is essential for businesses to adopt ethical frameworks and practices in data-driven marketing. Prioritizing privacy and respecting customer preferences not only aligns with moral principles but also contributes to long-term customer satisfaction and loyalty. Case studies and examples Here are some case studies and examples of data-driven marketing campaigns, industries and companies benefiting from data-driven marketing and best practices to follow Successful data-driven marketing campaigns. These companies have effectively utilized data-driven marketing strategies to create targeted campaigns with positive outcomes and the ethical practices they implemented to ensure customer privacy. used data to improve the user experience of its mobile app. The company found that most users were interested in buying insurance directly from the app, so they added a “buy” feature. This resulted in a $2 billion increase in written premiums within a year. Progressive Insurance used data to personalize its marketing campaigns. The company found that customers receiving personalised emails were likelier to open and click on them. This resulted in a 10% increase in email click-through rate and a 5% increase in sales. Macy’s uses data to recommend movies and TV shows to its users. The company collects data on what its users watch, rate, and search for. This data is used to create personalized recommendations for each user. Netflix uses data to target its advertising. The company collects data on what its users search for, buy, and view. This data is used to show users relevant ads on other websites. Amazon Industries and companies benefiting from data-driven marketing Data-driven marketing is beneficial for businesses of all sizes in all industries. However, some industries and companies are particularly well-suited for data-driven marketing. These include: Retail Industry Retailers leverage big data to understand customer preferences and buying patterns. They use personalized marketing strategies to offer tailored product recommendations and promotions. This approach enhances customer engagement and loyalty, resulting in increased sales and revenue. Financial Services Sector Banks and financial institutions utilize data-driven marketing to personalize their offerings. By analyzing transaction history, income levels, and spending patterns, targeted financial products are recommended. This data-driven approach helps financial institutions improve customer acquisition and retention. Travel and Hospitality Industry Travel companies harness big data to create personalized travel experiences. By analyzing customer data, including travel preferences and past bookings, tailored recommendations are provided. This enables travel companies to deliver customized offers, resulting in increased bookings and customer satisfaction. Technology Companies Technology companies utilize data-driven marketing to understand customer needs and preferences. By analyzing user behavior, product usage patterns, and feedback, they can improve product development and enhance customer experiences. Data-driven marketing enables technology companies to target specific customer segments with relevant messaging and promotions. Media Organizations Media companies leverage data-driven marketing to deliver personalized content to their audiences. By analyzing user preferences, content consumption patterns, and engagement metrics, they can tailor content recommendations and advertising. This data-driven approach enhances user engagement, drives subscriptions, and maximizes advertising revenue. Telecommunications Sector Telecommunications companies utilize big data to understand customer usage patterns and preferences. By analyzing call records, data usage, and customer feedback, they can offer personalized plans and targeted promotions. Data-driven marketing helps telecommunications companies increase customer satisfaction, reduce churn, and drive revenue growth. These sectors and businesses appreciate the importance of data-driven marketing in knowing their consumers, providing tailored experiences, and improving their marketing efforts. They acquire a competitive advantage, promote client engagement, and accomplish corporate success by using the power of big data. Conclusion As we leverage the potential of big data, we must keep ethical issues at the forefront of our marketing operations. Respecting client privacy, guaranteeing data security, and offering transparency and control over data use are critical for establishing and sustaining consumer confidence. Looking forward, the future of data-driven marketing offers even greater promise. Marketers may anticipate increasingly advanced data analysis tools and automation capabilities as new technologies such as artificial intelligence and machine learning evolve. Furthermore, the changing environment of data governance and legislation will need organizations staying current and compliant in order to preserve client privacy while successfully using data. Finally, data-driven marketing provides a method to realise big data's full potential for customized advertising. Businesses may build individualized, powerful marketing experiences that drive success in the ever-competitive digital marketplace by exploiting the insights obtained from data analysis and implementing ethical procedures. Adopting data-driven initiatives is not just a means of staying ahead, but also of connecting with consumers on a deeper level and fostering long-term connections that promote sustainable development.

Data-driven Marketing: Unleashing the Power of Big Data for Targeted Campaigns

As regulators the world over continue to , the insanely popular short-video app owned by Chinese company ByteDance, it seems they are missing the bigger picture. is only the tip of the iceberg; , and may potentially be used to influence users. scrutinize TikTok TikTok four out of the five most downloaded apps in April 2023 had Chinese roots This is not to say that something nefarious is happening on these apps — or even will happen. Chinese apps tend to be naturally successful in the West because they are created in a environment where engineers abide by the — work from 9 am to 9 pm, six days a week. The digital services that break out are good and thus successful. highly competitive 996 working hour system really Nevertheless, seeing how things are going for , which has been accused of posing a national security threat and censoring content unfavorable to China, it’s important to know what these apps are so that we may weigh in when they inevitably come to the forefront of international discussion. TikTok Temu — Amazon on steroids Temu is the American off-shoot of (拼多多), one of China’s largest e-commerce platforms. Both belong to , a Chinese conglomerate. Temu’s success comes from its ability to offer incredibly cheap products — “ ” — for a wide variety of categories from fashion ( ) to technology ( ), and everything in between. Pinduoduo PDD Holding exclusive sales up to 90% off $1.69 for five pairs of socks! $8.98 for Lenovo earbuds! Products can take a few weeks to arrive — — as they are shipped from Chinese factories or warehouses. This reduces the price drastically, so users seem not to mind. Growth has been driven by aggressive marketing campaigns, including . if they ever do directly a Superbowl advert in early 2023 This all sounds very reasonable, and even bears a striking resemblance to s . But an over-zealously Sinophobic lawmaker could find plenty to say about Temu. For starters, the app collects much more personal data from its users than TikTok does. Names, addresses, phone numbers, email addresses, credit card information, browsing history, preferences, and purchases could all potentially be used by unfriendly actors to target users with phishing scams, identity theft, fraud, blackmail, or espionage. Amazon’ modus operandi The real “threat”, however, is much more straightforward. For the first time in 30 years, China’s manufacturing has become capable enough to entirely do away with intermediaries and directly compete with Western companies ( ). If more and more consumers flock to Temu to buy cheap goods, and other competitors might feel pressured to slash their prices too. A lower break-even point — combined with shareholders’ demands for short-term profits — would cut wages, erode domestic manufacturing, and create (further) unrest within the more blue-collar parts of the population. As we know, ; best to keep an eye out. RIP drop-shipping Amazon they’re already pretty upset CapCut — TikTok’s talented younger brother Another insanely popular Chinese app is (originally JianYing), a video-editing app launched and maintained by ByteDance, . The app offers basic video editing features such as text, stickers, filters, colors, and music, as well as more advanced features such as keyframe animation, smooth slow-motion effects, chroma key, Picture-in-Picture (PIP), and stabilization. CapCut the same company behind TikTok CapCut prides itself in being “the official free Video Editor and Video Maker with Music for TikTok that is versatile and easy-to-use”. It’s no “Just Do It”, but the lack of a good tagline hasn’t stopped the app’s growth: it now has , mostly driven by TikTok’s success and the need to keep innovating to stay at the top of the virality game. 200 million monthly active users Nothing there could be concerning for lawmakers, right? That is, until they learn that . Notably, one of the tools being discussed would allow users to generate videos in seconds simply by providing a textual prompt. multiple teams at CapCut are working to launch AI-driven features This presents two issues that anti-China governments could exploit. First, as fake videos and begin to cause harm, it’s likely that foreign tools will be heavily scrutinized for any signs of government intervention. It would be too easy to train an AI model that makes particularly believable fakes of one political candidate over another, or to ensure that specific power dynamics are highlighted in all AI-generated content, slowly but surely encouraging unrest. become increasingly common The topic of data privacy is also likely to quickly become preponderant. Many people are already displeased about AIs being trained with their image and content without ever seeing a dime. That feeling is likely to become somewhat exaggerated when the AIs are created by foreign powers to do who knows what. SHEIN — Our planet’s worse nightmare You’ve heard of SHEIN. And if you haven’t your teenage daughter has. As have the entire world’s teenage daughters, it seems. The app is that specializes in women’s fashion, offering “trendy” clothing, shoes, accessories, and beauty products at affordable prices. Affordable here means dirt-cheap, this being a Chinese company. yet another e-commerce company It is owned by Roadget Business Pte. Ltd., a Singapore-based subsidiary of ZOETOP Business Co., Ltd., a Chinese company based in Nanjing that also operates other fashion platforms such as ROMWE and CUPSHE. If this level of complexity seems suspicious, trust your instincts. According to a compiled by Money.co.uk, SHEIN has taken over giants like Nike and Adidas as the most-Googled clothing brand, and Zara and Macy’s in online sales. Just like Temu, the company has been using aggressive advertising over the past couple of months to get a significant share of the fast fashion market. This includes ( ), pop-up shops, and even a social media reality show co-hosted by Khloé Kardashian. In fact, their posters are plastered all over the Paris metro — that’s often the surest way of knowing a company is on a European blitz. report influencer partnerships #SHEINhaul SHEIN could be accused of the same “crimes” as Temu. It collects plenty of information about users and is lowering the standards and average prices of fast fashion so drastically that Zara and H&M are starting to look like Balenciaga in comparison. However, SHEIN has been targeted by more serious allegations already in the West. Firstly, the company is accused of . This has been an ongoing source of friction with China and is likely to get more intense as Sinophobia gets more intense (especially within luxury-heavy countries like Italy and France). providing counterfeit goods Secondly, the company’s treatment of workers is famously appalling: from , every rule in the book is broken by SHEIN. Finally, and perhaps more importantly, the environmental impact of SHEIN has come to the forefront. The company (and/or its affiliates) the same amount of CO2 as approximately 180 coal-fired power plants unsafe conditions to unsustainable hours churns out . Should there be an attack on the company, these will likely be the angle chosen by regulators. Given that the world is both increasingly warm and covered in trash, who could blame them? Temu, CapCut, and SHEIN have all . And why wouldn’t they? Based on how TikTok has been treated by US regulators who have tried to ban it or force it to sell its US operations to an American company, they have every reason to downplay their Chinese connections. But it may not be enough. distanced themselves from China in recent months As mentioned above, they could face accusations of being linked to or controlled by the Chinese government, of collecting or sharing user data with China for espionage or propaganda purposes, of violating user privacy or security rights, of infringing on intellectual property rights of other companies or countries, or of posing a competitive threat to local businesses or industries. These accusations may lead to various consequences for these apps and their users. They could be banned or restricted from operating in certain markets or platforms. They could be sued or fined for violating laws or regulations. They could be boycotted or criticized by consumers or media outlets. They could lose access to suppliers or partners that are affected by sanctions or tariffs. They could face technical difficulties or glitches due to cyber-attacks or sabotage. Only time will tell if this comes to be. In the meantime, and probably more importantly, we may need to wonder why some of the best apps out there are Chinese. Are we losing our edge? Maybe there are lessons to be learnt here, rather than relying on fear. As Kai-Fu Lee, a former executive at and who now runs an AI-focused venture capital firm in Beijing, said in a recent interview: “ ”. Perhaps we should pay more attention to what these apps are doing right, rather than what they are doing wrong. Google Microsoft China has shown the world that innovation can happen anywhere Good luck out there. Also published here.

Chinese Apps in Crosshairs: Will SHEIN, CapCut and Temu Face Regulations Next?

E-commerce has seen an incredible boom in recent years due to COVID. But even as the pandemic restrictions were mostly lifted across the globe, the conveniences of online shopping are here to stay, and . Morgan Stanley predicts that the e-commerce market will grow . the sector continues growing from $3.3 trillion in early 2022 to $5.4 trillion in 2026 On the other hand, the banking sector is seeing a decline amid rising interest rates, high inflation, and uncertain perspectives for business activity. The red warning light flashed bright in March 2023, as the 16th largest bank in the US, Silicon Valley Bank (SVB), after a bank run. Amid the rising panic, smaller players like Signature Bank . Even the biggest players cannot be regarded as safe anymore, as the Credit Suisse giant was recently taken over . The world’s banking majors like and report profits shrinking by as much as 20%. collapsed followed by its peer, UBS JP Morgan Citigroup At the crossroads of these two major trends, the e-commerce boom and the banking sector turmoil — lies an opportunity known as Banking-as-a-Service (BaaS) and Embedded Finance. Rising tech companies are creating innovative products, like marketplaces and aggregators that, at some point, can leverage financial services. Banks are seeking ways to boost their revenue and client base. Blending these needs is a win-win situation for both sides. According to McKinsey’s estimates, embedded finance market revenue reached $20 billion in the US alone in 2021 and could still . Another estimate by suggests that this segment would grow to over $187 billion globally by 2027, while BaaS platform revenues are set . double within the next three to five years Juniper Research to reach $38 billion, up from $11 billion in 2022 How it works Digital life is becoming more and more complicated, so it is no surprise that customers are increasingly looking for all-in-one solutions. Brought to the pinnacle, this logic leads us to giant ecosystems, combining goods, services, and finance in one seamless experience. As early as 2020, the world’s leading companies were ecosystems. The path to an ecosystem for the biggest players is obvious: acquiring their financial service providers and creating proprietary in-house solutions that would be tailored to the company’s internal systems and demands from the start. This was the strategy pursued by major retailers like and . In a lower league, the need for seamless solutions is satisfied by cooperation with BaaS providers to create an embedded finance experience for customers. Classic banks own and operate the entire value chain, from the banking license to engaging end-user B2B and B2C customers. In BaaS, the value chain is split between partnering companies, enabling them to focus on what they do best and make profits from it. There are (and an infinite number of blends between them) of BaaS services: Walmart IKEA four major models offer their banking licence and products, operations, and/or technology for use by aggregators, other banks, and non-financial companies. One example of this model is Uber, offering its proprietary debit card to new drivers and delivery partners. With this card, they can instantly use money paid for the journeys they performed. Also, they can benefit from cashbacks and incentives from Uber partnering vendors. Another example is SoFi which offers investment products as well as loans in one financial super app. Also, clients can opt for a debit card issued by Bancorp to complete their finance management package. Providers benefit from BaaS by offering banking services to customers of distributor partners, scaling existing assets, and generating incremental revenue at a minimal cost. Providers act like Providers, but also lend their capabilities to other vendors to compose a complete out-of-the-box solution. Shopify is one example of this model, integrating small businesses’ accounts held at Evolve Bank & Trust with an e-commerce platform. Clients are offered debit cards and tools for monitoring their finances. Cash App by Square is another example, offering peer-to-peer payments, investment and cryptocurrency trade. Cards are offered via Marqeta and its banking partner, Sutton Bank. By aggregating other providers, provider-aggregators can offer integrated financial products, which can deepen relationships with distributors. Providers-Aggregators leverage end-customer relationships to offer unique financial services propositions. This model is employed by global players like Facebook, Amazon, and eBay to integrate financial services into marketplaces and ecosystems; by Xero and Shopify to offer B2B services; by retail giants like IKEA, Macy’s and Walmart to offer point-of-sale lending, loyalty programs and co-branded financial services. Finally, it enables players like Wealthfront and Aspiration to offer digital banking and niche fintech services. In this model, providing banking services creates new value for customers, enabling distributor partners to strengthen their core business. Distributors enhance the propositions they distribute by adding new products or technology from multiple providers. For instance, Google Pay offers a digital wallet for conventional and online purchases as well as peer-to-peer payments. Also, it offers tools for finance monitoring and checking and saving accounts with several banking partners. The Lending Club enables small businesses to open and manage their accounts, including payment acceptance. Accounts are enabled by several fintech partners like Alloy for verification and KYC, Treasury Prime for lifecycle APIs and Marqeta for card issuance. By aggregating providers, these players offer the elevated experiences that customers are demanding; for non-financial companies this model enables them to distribute banking services without becoming a bank. Distributor-Aggregators Choosing the right model and partner The primary question is: how are you going to grow your business and what is the primary source of your income. Benefits do not distribute equally along the value chain, so it is important to find the right place in it. Most of it concentrates on the risk-bearing side of providers and on the customer-relationship side of distributors. According to , the majority of revenues from embedded-finance lending products, 55% of the $14 billion in the United States in 2021, accrued to the balance sheet provider, the firm bearing the risk of credit default. However, with payments and deposits, it was the distributors who owned the end-customer relationship who benefited most. In lending, for instance, they earned $4 billion of the remaining $6 billion revenue pool, equal to 30 percent of total revenues. McKinsey research Choosing the right partner among banks may point away from the biggest players. For banks with full-fledged proprietary delivery networks, embedded finance may pose a . However, banks with a smaller retail presence, such as community banks and regional banks, may see it as a good opportunity to grow without making hefty investments in building their own sales funnels. cannibalization risk Another thing to keep in mind is whether you should choose to partner with a BaaS platform provider directly or opt for cooperating with a fintech intermediary to offer embedded finance. Samuel Head, head of EMEA banking sales at Currencycloud, a provider of cross-border multi-currency transaction solutions, suggests a . simple rule of thumb “A company considering embedding financial services simply needs to consider how much ownership they wish to have. And what their end users’ needs are. In the main, if you are not in FinTech, find a FinTech company you can trust to embed a quality service that helps you sell more of your core product or service,’ Head told . “If you are a FinTech, think about using a BaaS supplier. It’s actually a good time to build in redundancy and to be very careful when selecting your partners.” Fintech Global

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